caffe的简单python脚本
#-*-coding:utf-8-*-
import caffe
import matplotlib.pyplot as plt
#网络构建
caffe.set_mode_cpu() # 设置caffe为cpu模式，也可设成gpu模式
model_def = 'my_alexnet_test.prototxt'
model_weights = 'models_rist1/lrcn_four_words_iter_10000.caffemodel'
net = caffe.Net(model_def, # 定义模型结构
model_weights, # 包含模型训练权重
caffe.TEST) # 使用测试模式(训练中不能执行dropout)
#读取图像
image = caffe.io.load_image('./images/COCO_train2014_000000246146.jpg')
#图像处理
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1)) # 将图像通道数设置为outermost的维数
transformer.set_raw_scale('data', 255) # 像素值从[0,1]变换为[0,255]
transformer.set_channel_swap('data', (2,1,0)) # 交换通道，RGB->BGR
#transformer.set_mean('data', channel_mean)
transformed_image = transformer.preprocess('data', image)
#图像可视化
plt.imshow(image)
plt.show()
#前向
net.blobs['data'].data[...] = transformed_image
output = net.forward()
net.forward()
# 循环打印每一层名字和相应维度
#for layer_name, blob in net.blobs.iteritems():
# print layer_name + '\t' + str(blob.data.shape)
#打印输出
#for layer_name, param in net.params.iteritems():
# print layer_name + '\t' + str(param[0].data.shape), str(param[1].data.shape)
#print '**********'
#filters = net.params['conv1'][0].data
#打印层的输出
print transformed_image
print '**********'
#print transformed_image
#feat = net.blobs['conv1'].data[0, :36]
#print feat